Werner von Siemens Award 2023 - 6th place in Industry 4.0 category

Our student Erik Pasztor won 6th place in Industry 4.0 category at the Werner von Siemens Award 2023 with the diploma thesis about Edge machine learning-based industrial fault detection.

The thesis describes the implementation of machine learning-based fault detection on an edge device. The main part of the system is built on an STM32F413ZH microcontroller that performs data acquisition and processing and inference by a pre-trained machine-learning model. A gearbox was 3D printed for demonstration of this device with interchangeable undamaged and damaged wheels. Apart from this, the designed demonstration unit is composed of an electric motor, a microcontroller to control it, a separate microcontroller to enable Ethernet communication and a display. The proposed system collects data from an incremental rotary encoder, preprocesses the signal, and extracts features based on both its frequency and time domain characteristics. Various models trained in NanoEdge AI Studio were tested and compared, and the limit where the system can still reliably detect faults was determined. With anomaly detection, a true positive rate of 1 and a true negative rate of 0.74 were achieved. With multiclass classification, a perfect score was obtained when considering only the healthy state and two faults.